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Record W4409119845 · doi:10.1139/gen-2024-0164

Reducing the regulatory burden of plant biotechnology regulations in Canada

2025· article· en· W4409119845 on OpenAlexaffvenueabout
Simona Lubieniechi, Savannah Gleim, Stuart J. Smyth

Bibliographic record

VenueGenome · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCommercializationBiotechnologyAgricultural biotechnologyBusinessAgricultureConsumption (sociology)Product (mathematics)Pipeline (software)Emerging technologiesProduction (economics)Natural resource economicsIndustrial organizationInternational tradeMarketingBiologyEconomicsEngineering

Abstract

fetched live from OpenAlex

Regulations within the crop agriculture industry exist to ensure that products undergoing risk assessment prior to commercialization are safe for the environment and human consumption. Since 1995, these regulations have provided safe crops and foods for Canadians to consume, as no commercialized innovative product has caused any post-commercialization health or environmental problems. However, Canada suffers from a gap in its innovation pipeline in that far more investments go into the innovation pipeline than products come out. Canada is a global top ten nation in terms of innovation investments yet drops over ten positions when it comes to outputs. Additionally, Canada is one of the lowest ranked on the G30 list of countries in terms of regulatory burden on the economy. This article describes updates to the regulatory framework for plant biotechnology, highlighting recent changes regarding regulation of gene editing technologies and how these changes respond to previously identified innovation barriers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.207
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2025
Admission routes3
Has abstractyes

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